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Evaluation on regional science and technology resources allocation in China based on the zero sum gains data envelopment analysis

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Abstract

Regional science and technology (S&T) resource allocation is an important supporting means of Intelligent Manufacturing in the future. Research on the efficiency of S&T resource allocation is helpful to judge the potential of Intelligent Manufacturing in a specific region. S&T performance evaluation and resource allocation are critical administrative activities for a country or region. Due to resource scarcity, it is necessary to consider the constraint of limited total resources in the process of evaluation and allocation. Thus, the zero sum gains data envelopment analysis models and the associated uniform frontier (UF) method are more suitable for this issue. Comparing with the existing methods, we propose a new algorithm for solving the UF method in this article, which simplifies the procedure of calculation and extends from single to multiple resource allocation. In the empirical application, we evaluate the S&T performances and allocate R&D personnel and intramural expenditure among 31 administrative regions in China. There are 10 high-performance regions. Results can provide specific reference meanings to policy making and analysis.

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Acknowledgements

The study was co-funded by the National Key Research and Development Program of China (2018YFC1902703), National Social Science Foundation of China (18CGL027), Beijing Social Science Foundation (16YJC042) and the 2018 International Clean Energy Talent Program.

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Authors and Affiliations

  1. Institute of Circular Economy, Beijing University of Technology, Beijing, 100124, China

    Tingting Liu & Zichen Zheng

  2. College of Economics and Management, Anhui Agricultural University, Hefei, 230036, China

    Yuneng Du

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  1. Tingting Liu
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  2. Zichen Zheng
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  3. Yuneng Du
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Correspondence to Yuneng Du.

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Liu, T., Zheng, Z. & Du, Y. Evaluation on regional science and technology resources allocation in China based on the zero sum gains data envelopment analysis. J Intell Manuf 32, 1729–1737 (2021). https://doi.org/10.1007/s10845-020-01622-w

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  • DOI: https://doi.org/10.1007/s10845-020-01622-w

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